ken11o2/ai-voice-cloning
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add args.use_deepspeed

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add DeepSpeed feature
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ken11o2 2023-09-04 18:53:22 +00:00
parent 5d87418d3a
commit 57f8da7802
1 changed files with 3961 additions and 3957 deletions
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6
src/utils.py
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@ -3272,6 +3272,7 @@ def setup_args(cli=False):
'embed-output-metadata': True,
'latents-lean-and-mean': True,
'voice-fixer': False, # getting tired of long initialization times in a Colab for downloading a large dataset for it
'use-deepspeed': True,
'voice-fixer-use-cuda': True,
@ -3330,6 +3331,7 @@ def setup_args(cli=False):
parser.add_argument("--latents-lean-and-mean", action='store_true', default=default_arguments['latents-lean-and-mean'], help="Exports the bare essentials for latents.")
parser.add_argument("--voice-fixer", action='store_true', default=default_arguments['voice-fixer'], help="Uses python module 'voicefixer' to improve audio quality, if available.")
parser.add_argument("--voice-fixer-use-cuda", action='store_true', default=default_arguments['voice-fixer-use-cuda'], help="Hints to voicefixer to use CUDA, if available.")
parser.add_argument("--use-deepspeed", action='store_true', default=default_arguments['use-deepspeed'], help="Use deepspeed for speed bump.")
parser.add_argument("--force-cpu-for-conditioning-latents", default=default_arguments['force-cpu-for-conditioning-latents'], action='store_true', help="Forces computing conditional latents to be done on the CPU (if you constantyl OOM on low chunk counts)")
parser.add_argument("--defer-tts-load", default=default_arguments['defer-tts-load'], action='store_true', help="Defers loading TTS model")
parser.add_argument("--prune-nonfinal-outputs", default=default_arguments['prune-nonfinal-outputs'], action='store_true', help="Deletes non-final output files on completing a generation")
@ -3414,6 +3416,7 @@ def get_default_settings( hypenated=True ):
'embed-output-metadata': args.embed_output_metadata,
'latents-lean-and-mean': args.latents_lean_and_mean,
'voice-fixer': args.voice_fixer,
'use-deepspeed': args.use_deepspeed,
'voice-fixer-use-cuda': args.voice_fixer_use_cuda,
'concurrency-count': args.concurrency_count,
'output-sample-rate': args.output_sample_rate,
@ -3467,6 +3470,7 @@ def update_args( **kwargs ):
args.latents_lean_and_mean = settings['latents_lean_and_mean']
args.voice_fixer = settings['voice_fixer']
args.voice_fixer_use_cuda = settings['voice_fixer_use_cuda']
args.use_deepspeed = settings['use_deepspeed']
args.concurrency_count = settings['concurrency_count']
args.output_sample_rate = 44000
args.autocalculate_voice_chunk_duration_size = settings['autocalculate_voice_chunk_duration_size']
@ -3639,7 +3643,7 @@ def load_tts( restart=False,
print("!!!! WARNING !!!! No GPU available in PyTorch. You may need to reinstall PyTorch.")
print(f"Loading TorToiSe... (AR: {autoregressive_model}, diffusion: {diffusion_model}, vocoder: {vocoder_model})")
tts = TorToise_TTS(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, diffusion_model_path=diffusion_model, vocoder_model=vocoder_model, tokenizer_json=tokenizer_json, unsqueeze_sample_batches=args.unsqueeze_sample_batches)
tts = TorToise_TTS(minor_optimizations=not args.low_vram, autoregressive_model_path=autoregressive_model, diffusion_model_path=diffusion_model, vocoder_model=vocoder_model, tokenizer_json=tokenizer_json, unsqueeze_sample_batches=args.unsqueeze_sample_batches, use_deepspeed=args.use_deepspeed)
elif args.tts_backend == "vall-e":
if valle_model:
args.valle_model = valle_model